Course Outline

ICT706 Machine Learning

Course Coordinator:Damian Hills (dhills1@usc.edu.au) School:School of Science, Technology and Engineering

2022Semester 1

Online

Online You can do this course without coming onto campus.

Please go to the USC website for up to date information on the
teaching sessions and campuses where this course is usually offered.

What is this course about?

Description

Massive amounts of data are collected in almost every corner of the world, and they become the new strategic mechanisms for intelligent businesses. This course covers both foundational knowledge and more advanced practical skills about data processing and analysis. It explores the use of, and techniques used in, exploratory, descriptive, and predictive analytics. Combining technical and statistical skills, analytical thinking, and business acumen, it helps you to harness the power of data analytics.

How will this course be delivered?

Activity Hours Beginning Week Frequency
Online
Learning materials – Pre-recorded concept videos and associated activity 1hr Week 1 12 times
Tutorial/Workshop 1 – Interactive zoom tutorial 2hrs Week 1 12 times

Course Topics

Introduction to data analytics and data science

Data quality issues and pre-processing

Exploratory data analysis and visualisation

Data relationships: association rules and clustering

Machine learning: linear regression, decision trees, deep learning, artificial neural networks

 

What level is this course?

700 Level (Specialised)

Demonstrating a specialised body of knowledge and set of skills for professional practice or further learning. Advanced application of knowledge and skills in unfamiliar contexts.

What is the unit value of this course?

12 units

How does this course contribute to my learning?

Course Learning Outcomes On successful completion of this course, you should be able to... Graduate Qualities Completing these tasks successfully will contribute to you becoming...
1 Demonstrate a specialised and integrated understanding of contemporary data science and business analytics theories and practices. Knowledgeable
Empowered
2 Use data mining, machine learning and data analysis techniques to develop relevant and rigorous models to gain business insights. Knowledgeable
Creative and critical thinker
3 Investigate, evaluate, and plan the lifecycle of data through an organisation. Knowledgeable
4 Apply computer technology in the solution of business analytics problems. Creative and critical thinker
Engaged

Am I eligible to enrol in this course?

Refer to the USC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.

Pre-requisites

ICT703

Co-requisites

Not applicable

Anti-requisites

Not applicable

Specific assumed prior knowledge and skills (where applicable)

Not applicable

How am I going to be assessed?

Grading Scale

Standard Grading (GRD)

High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL).

Details of early feedback on progress

Feedback will be provided for the formative exercises in the weekly computer workshops. This feedback will give students immediate feedback on their understanding and progress in the course.

Assessment tasks

Delivery mode Task No. Assessment Product Individual or Group Weighting % What is the duration / length? When should I submit? Where should I submit it?
All 1 Examination - not Centrally Scheduled Individual 30%
2 hours
Week 6 Online Test (Quiz)
All 2 Artefact - Technical and Scientific, and Written Piece Individual 30%
2,000 words
Week 11 Online Assignment Submission with plagiarism check
All 3 Examination - Centrally Scheduled Individual 40%
2 hours
Exam Period Online Test (Quiz)
All - Assessment Task 1:Data analytics test
Goal:
To learn about the concepts of data analytics using hands on tools. This task enables you to apply computer tools to solve business problems
Product: Examination - not Centrally Scheduled
Format:
You will be presented with a data-related challenge, and will use computer tools to manipulate data to solve that challenge. This task will help to build your knowledge of data analysis skills. Further details will be available on Canvas.
Criteria:
No. Learning Outcome assessed
1
Use of data analytics
2
2
Data processing and analysis of computer problems
4
3
Manipulation of data to solve problems
3
All - Assessment Task 2:Research project
Goal:
To undertake a data analytics approach to solve a set of business problems that require the use of appropriately selected data processing and mining approaches.
Product: Artefact - Technical and Scientific, and Written Piece
Format:
This is an individual assessment. The assessment will report the set of business problems, data required, and data mining tools selected to solve the selected problems. Further details will be available on Canvas.
Criteria:
No. Learning Outcome assessed
1
Clear statement of the business problems and their dependence on data analysis
4
2
Analysis of how data analysis methods are used in an organisation
2
3
Clear summary of relevant information
1
All - Assessment Task 3:Final Examination
Goal:
This assessment task will demonstrate your knowledge and application of all material covered in this course.
Product: Examination - Centrally Scheduled
Format:
A final examination will be held in the examination period. This two-hour examination will consist of a series of short answer questions to test understanding and application of concepts. This is an individual assessment.
Criteria:
No. Learning Outcome assessed
1
Your responses on the exam questions will be assessed on the basis of your demonstration of knowledge and application from the workshop activities, lectures and readings specified during the lecture series.
1 3

Directed study hours

A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Directed study hours may vary by location. Student workload is calculated at 12.5 learning hours per one unit.

What resources do I need to undertake this course?

Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.

Prescribed text(s) or course reader

Please note that you need to have regular access to the resource(s) listed below. Resources may be required or recommended.

Required? Author Year Title Edition Publisher
Required Foster Provost,Tom Fawcett 2013 Data Science for Business n/a Oreilly & Associates Incorporated

Specific requirements

Not applicable

How are risks managed in this course?

Health and safety risks for this course have been assessed as low. It is your responsibility to review course material, search online, discuss with lecturers and peers and understand the health and safety risks associated with your specific course of study and to familiarise yourself with the University’s general health and safety principles by reviewing the online induction training for students, and following the instructions of the University staff.

What administrative information is relevant to this course?

Assessment: Academic Integrity

Academic integrity is the ethical standard of university participation.  It ensures that students graduate as a result of proving they are competent in their discipline.  This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.

Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person.  You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.

In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin.  This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.

Assessment: Additional Requirements

Eligibility for Supplementary Assessment

Your eligibility for supplementary assessment in a course is dependent of the following conditions applying:

The final mark is in the percentage range 47% to 49.4%
The course is graded using the Standard Grading scale
You have not failed an assessment task in the course due to academic misconduct

Assessment: Submission penalties

Late submission of assessment tasks may be penalised at the following maximum rate: 
- 5% (of the assessment task's identified value) per day for the first two days from the date identified as the due date for the assessment task. 
- 10% (of the assessment task's identified value) for the third day - 20% (of the assessment task's identified value) for the fourth day and subsequent days up to and including seven days from the date identified as the due date for the assessment task. 
- A result of zero is awarded for an assessment task submitted after seven days from the date identified as the due date for the assessment task. Weekdays and weekends are included in the calculation of days late. To request an extension you must contact your course coordinator to negotiate an outcome.

SafeUSC

USC is committed to a culture of respect and providing a safe and supportive environment for all members of our community. For immediate assistance on campus contact SafeUSC by phone: 07 5430 1168 or using the SafeZone app. For general enquires contact the SafeUSC team by phone 07 5456 3864 or email safe@usc.edu.au.

The SafeUSC Specialist Service is a Student Wellbeing service that provides free and confidential support to students who may have experienced or observed behaviour that could cause fear, offence or trauma. To contact the service call 07 5430 1226 or email studentwellbeing@usc.edu.au.

Study help

For help with course-specific advice, for example what information to include in your assessment, you should first contact your tutor, then your course coordinator, if needed.

If you require additional assistance, the Learning Advisers are trained professionals who are ready to help you develop a wide range of academic skills. Visit the Learning Advisers web page for more information, or contact Student Central for further assistance: +61 7 5430 2890 or studentcentral@usc.edu.au.

Wellbeing Services

Student Wellbeing provide free and confidential counselling on a wide range of personal, academic, social and psychological matters, to foster positive mental health and wellbeing for your academic success.

To book a confidential appointment go to Student Hub, email studentwellbeing@usc.edu.au or call 07 5430 1226.

AccessAbility Services

Ability Advisers ensure equal access to all aspects of university life. If your studies are affected by a disability, learning disorder mental health issue, injury or illness, or you are a primary carer for someone with a disability or who is considered frail and aged, AccessAbility Services can provide access to appropriate reasonable adjustments and practical advice about the support and facilities available to you throughout the University.

To book a confidential appointment go to Student Hub, email AccessAbility@usc.edu.au or call 07 5430 2890.

Links to relevant University policy and procedures

For more information on Academic Learning & Teaching categories including:

  • Assessment: Courses and Coursework Programs
  • Review of Assessment and Final Grades
  • Supplementary Assessment
  • Administration of Central Examinations
  • Deferred Examinations
  • Student Academic Misconduct
  • Students with a Disability

Visit the USC website: https://www.usc.edu.au/explore/policies-and-procedures#academic-learning-and-teaching

Student Charter

USC is committed to excellence in teaching, research and engagement in an environment that is inclusive, inspiring, safe and respectful. The Student Charter sets out what students can expect from the University, and what in turn is expected of students, to achieve these outcomes.

General Enquiries

  • In person:
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    • USC Moreton Bay - Service Centre, Ground Floor, Foundation Building, Gympie Road, Petrie
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  • Email:studentcentral@usc.edu.au